LP Gamma (LPG) Metrics Calculation

This function calculates various LP Gamma (LPG) metrics for a specified reference level, such as a protein, a gene or a protein group. The metrics are derived from the coLogarithm of Probability (LP) of their constituting peptides and include probabilities derived from maximum LP, sum of LP values, and filtered LP sums using a specified peptide-level FDR threshold.

Usage

lpg(data, levelRef, threshold = 0.01)

Arguments

data

A data frame containing identification data, including peptide-related columns for coLogarithm of Probability (LP), q-values (qval), and a logical column isDecoy.

levelRef

The column name of the reference level to group by, such as a protein or a gene identifier. This should be an unquoted column name.

threshold

A numeric value representing the FDR threshold for peptide-level q-values (default is 0.01).

Value

A data frame containing the calculated LPG metrics:

isDecoy

Indicates whether the group contains any decoy identification.

n

The total number of peptide identifications for the group.

m

The number of peptide identifications with a q-value below the threshold.

LPM

The maximum coLogarithm of Probability (LP) for the group.

LPS

The sum of coLogarithm of Probability (LP) for the group.

LPF

The sum of coLogarithm of Probability for identifications with a q-value below the threshold.

LPGM

The LP Gamma value based on the maximum LP.

LPGS

The LP Gamma value based on the sum of LP values.

LPGF

The LP Gamma value based on the filtered sum of LP values for confident identifications.

See also

• Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics for more information on the LPG scores.

Examples

# Example usage with a sample dataset
sample_data <- data.frame(
  levelRef = c("P1", "P1", "P2", "P2", "P3"),
  LP = c(1.5, 2.0, 0.5, 1.0, 1.2),
  qval = c(0.01, 0.02, 0.005, 0.03, 0.01),
  isDecoy = c(FALSE, FALSE, TRUE, FALSE, FALSE)
)
lpg(sample_data, levelRef, threshold = 0.01)
#> # A tibble: 3 × 10
#>   levelRef isDecoy     n     m   LPM   LPS   LPF  LPGM  LPGS  LPGF
#>   <chr>    <lgl>   <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 P1       FALSE       2     1   2     3.5   1.5 1.70  2.54  1.20 
#> 2 P2       TRUE        2     1   1     1.5   0.5 0.721 0.851 0.199
#> 3 P3       FALSE       1     1   1.2   1.2   1.2 1.2   1.2   1.2